A large-scale longitudinal study will be conducted of vulnerability and resilience among economically stressed parents who are striving to raise their children in rural settings. The mental health of parents is critically important, because distressed parents and those suffering from psychiatric disorders show significant impairment in parenting functions, which in turn, renders their children vulnerable to disorder. The proposed study builds upon and extends research previously conducted at the ISU Center for Family Research in Rural Mental Health. The sample in the proposed investigation will be racially and geographically diverse and will be drawn from communities that show a high level of economic disadvantage. Our primary outcome variable, parent mental health, will be assessed at both the level of symptoms and clinically significant diagnoses. A four-wave longitudinal design will be employed that allows us to test mediation, moderation, and reciprocal relations among variables over time. Analyses will be conducted to test for differences among ethnic, socioeconomic, and regional subgroups in the processes and circumstances that lead to good mental health among parents. An innovative component of the study is its inclusion of community-level, family-level, and individual-level variables. We will investigate the effects of community characteristics (e.g., percent below the poverty line, crime rates) on interpersonal relationships, personal outlook, chronic and acute stressors, and symptoms and syndromes of psychiatric disorder. We will also investigate the effects of economic hardship and related stressors on interpersonal relationships, personal outlook, and mental health. The contribution of personality and problem-solving skill to the success with which parents deal with these stressors will be examined closely, as will relationships with intimate partner and members of the social network. Sophisticated data analytic techniques for the analysis of multi-level data will be used to analyze the data, including hierarchical linear modeling and structural equation modeling with latent variables.